Methods and Tools for Virtual Semantic Integration of Data from Distributed Heterogeneous Sources

Cover Page

Cite item

Full Text

Abstract

The article is devoted to the natural language processing from distributed heterogeneous sources based on the principles of their virtual semantic integration. The main purpose of data integration is to provide the user with unified access to distributed data as a single virtual storage for performing natural language queries, regardless of the data storage format and location. The article discusses the main approaches focused on virtual semantic data integration, and describes the proposed concept of building an ontology driven instrumental environment based on Data Fabric technology, which allows to automate data processing via intermediate layer of ontologies in a unified form. The article describes NuCoBoShell that is the instrumental environment implementing the proposed approach. NuCoBoShell uses ontology-driven semantic integration mechanism to provide the answering, which, unlike traditional Internet answering services, provides the opportunity to obtain more pertinent answers automatically extracting the necessary information from not only heterogeneous web resources, but also text documents stored in accessible data warehouses and user's local computer without the need to copy data to a single repository.

About the authors

S. I. Chuprina

Perm State Humanitarian Pedagogical University

Email: chuprinas@inbox.ru
Perm

K. V. Gimasheva

Perm State University

Email: gimashevakv@mail.ru
Perm

References

  1. Tuzovskiy, A. F. and Yampolskiy, V. Z. (2011), "Integration of information using semantic web technologies", Problemy informatiki, no. 2, pp. 51-58.
  2. Ballard, C., Davies, N., Gavazzi, M., Stephani, J.and Lurie, M. (2003), IBM Informix: Integration through data federation, IBM International Technical Support Organizat, USA, available at: http://www.iiug.org/library/ids/technical/sg247032.pdf (Accessed: 30.06.2024).
  3. Patel, A., Debnath, N. C. and Bhushan, B. (2022), Semantic Web Technologies: Research and Applications, 1st ed., CRC Press, USA, 404 p. doi: 10.1201/9781003309420.
  4. Gruber, T. R. (1993), "A Translation approach to portable ontology specifications", Knowledge Acquisition, vol. 5, no. 2, pp. 199-220.
  5. Bolshakova, E. I., Vorontsov, K. V., Efremova, N. E., Klyshinskiy, E. S., Lukashevich, N. V. and Sapin, A. S. (2017), Avtomaticheskaya obrabotka tekstov na estestvennom yazyke i analiz dannyh [Automatic text processing in natural language and data analysis], HSE University, Moscow, Russia.
  6. Chuprina, S. I. (2023), "Using Data Fabric Architecture to Create Personalized Visual Analytics Systems in the Field of Digital Medicine", Scientific visualization, vol. 15(5), pp. 50-63. doi: 10.26583/sv.15.5.05.
  7. Naidenova, X. A. and Nevzorova, O. A. (2008), "Machine Learning for Natural Language Processing: Contemporary State", Uchehye zapiski Kazanskogo universiteta. Seriya Fiziko-matematicheskie nauki, no. 4, pp. 5-24.
  8. Nurutdinov, A. R. and Latypov, R. Kh. (2022), "Potentials of the bio-inspired approach in the development of artificial intelligence systems (trends review)", Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, vol. 164, no. 2-3, pp. 244-265. doi: 10.26907/2541-7746.2022.2-3.244-265.
  9. Semantic Web W3C. URL: https://www.w3.org/standards/ (Accessed: 30.06.2024).
  10. Calvanese, D., De Giacomo, G. and Lenzerini, M. (2001), "Ontology of integration and integration of ontologies", Proceedings of the 14th Int. Workshop on Description Logics (DL 2001), Stanford, CA, USA, 1-3 August 2001, vol. 49, pp. 10-19.
  11. Chuprina, S. I. and Gimasheva, K. V. (2024), "Using visual data analysis methods to identify the need for semantic data integration", Trudy Mezhdunarodnoy konferentsii po komputernoy grafike b mashinnomu zreniyu "GraphiCon", Omsk, Russia, 17-19 September 2024, pp. 389-402. doi: 10.25206/978-5-8149-3873-2-2024-389-402.
  12. Gomes-Perez, A., Fernandez-Lopez, M. and Corcho, O. (2004), Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web, 1st ed., Springer-Verlag, London. 403 p.
  13. Davies, J. (2010), "Lightweight Ontologies", Theory and Applications of Ontology: Computer Applications, pp. 197-229. doi: 10.1007/978-90-481-8847-5_9.
  14. Ryabinin, K. and Chuprina, S. (2015), "Development of ontology-based multiplatform adaptive scientific visualization system", Journal of Computational Science, Elsevier, vol. 10, pp. 370-381. doi: 10.1016/j.jocs.2015.03.003.
  15. Ryabinin, K., Chuprina, S. and Belousov, K. (2019), "Ontology-Driven Automation of IoT-Based Human-Machine Interfaces Development", in Rodrigues, J. (eds), Computational Science - ICCS 2019, ICCS 2019, Lecture Notes in Computer Science, vol. 11540, Springer, Cham, pp. 110-124. DOI: https://doi.org/10.1007/978-3-030-22750-0_9.
  16. Chuprina, S., Ryabinin, K., Matkin, K. and Koznov, D. (2022), "Ontology-Driven Visual Analytics Software Development", Programming and Computer Software, vol. 48, no. 3, pp. 208-214. DOI: https://doi.org/10.1134/S0361768822030033.
  17. Ryabinin, K., Chuprina, S. and Labutin, I. (2022), "Tackling IoT Interoperability Problems with Ontology-Driven Smart Approach", in Rocha, A., Isaeva, E. (eds), Science and Global Challenges of the 21st Century - Science and Technology, Perm Forum 2021, Lecture Notes in Networks and Systems, vol. 342, Springer, Cham, pp. 77-91. DOI: https://doi.org/10.1007/978-3-030-89477-1_9.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).